Hands-on Exercise 3 Part 2

4.1 Programming Animated Statistical Graphics with R

• Creating animated data visualisation by using gganimate and plotly r packages

• Learn how to reshape data by using tidyr package

• Process, wrangle and transform data by using dplyr package

4.1.1 Basic Concepts of animation

• Plots do not move

• Many individual plots are built and stitched together as movie frames

• Each frame is a different plot and subset drives the flow of the animation when stitched back together

4.1.2 Terminology

• Frame: Represents a different point in time or a different category

• Animation Attributes: Settings that control how the animation behaves

4.2 Getting Started

4.2.1 Loading the R packages

pacman::p_load(readxl, gifski, gapminder,
               plotly, gganimate, tidyverse)

4.2.2 Importing the data

col <- c("Country", "Continent")
globalPop <- read_xls("GlobalPopulation.xls",
                      sheet="Data") %>%
  mutate_at(col, as.factor) %>%
  mutate(Year = as.integer(Year))

4.3 Animated Data Visualisation: gganimate methods

• gganimate extends the grammar of graphics as implemented by ggplot2 to include the description of animation

• transition_*() - how data should be spread out and how it relates to itself across time

• view_*() - how the positional scales should change along the animation

• shadow_*() - how data from other points in time should be presented in the given point in time

• enter_*()/exit_*() - how new data should appear and how old data should disappear during the course of the animation

• ease_aes() - how different aesthetics should be eased during transitions

4.3.1 Building a static population bubble plot

ggplot(globalPop, aes(x = Old, y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young') 

4.3.2 Building the animated bubble plot

• Transition_time() of gganimate is used to create transition through distinct states in time (i.e. Year)

• ease_aes() is used to control easing of aesthetics: Default is linear. Other methods are: quadratic, cubic, quartic, quintic, sine, circular, exponential, elastic, back and bounce

ggplot(globalPop, aes(x = Old, y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young') +
  transition_time(Year) +       
  ease_aes('linear')          

4.4 Animated Data Visualisation: plotly

• both ggplotly() and plot_ly() support key frame animations through the frame argument/aesthetic

• Also support an ids argument/aesthetic to ensure smooth transitions between objects with the same if

4.4.1 Building an animated bubble plot: ggplotly() method

The Plot

The Code

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(aes(size = Population,
                 frame = Year),
             alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young')

ggplotly(gg)

Even though show.legend = FALSE argument was used, the legend still appears on the plot. To overcome this problem, theme(legend.position=‘none’) should be used.

The Plot

The Code

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(aes(size = Population,
                 frame = Year),
             alpha = 0.7) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young') + 
  theme(legend.position='none')

ggplotly(gg)

4.4.2 Building an animated bubble plot: plot_ly() method

The Plot

The Code

bp <- globalPop %>%
  plot_ly(x = ~Old, 
          y = ~Young, 
          size = ~Population, 
          color = ~Continent,
          sizes = c(2, 100),
          frame = ~Year, 
          text = ~Country, 
          hoverinfo = "text",
          type = 'scatter',
          mode = 'markers'
          ) %>%
  layout(showlegend = FALSE)
bp